% STD_CUSTOM - Returns custom measure for a dataset.
%
% Usage:
% >> [spec freqs] = std_custom(EEG, func, 'key', 'val', ...);
%
% Inputs:
% EEG - a loaded epoched EEG dataset structure
% func - a function handle that takes a data array as input
%
% Optional inputs:
% 'components' - [numeric vector] components of the EEG structure for which
% activation spectrum will be computed. Note that because
% computation of ERP is so fast, all components spectrum are
% computed and saved. Only selected component
% are returned by the function to Matlab
% {default|[] -> all}
% 'channels' - [cell array] channels of the EEG structure for which
% activation spectrum will be computed. Note that because
% computation of ERP is so fast, all channels spectrum are
% computed and saved. Only selected channels
% are returned by the function to Matlab
% {default|[] -> none}
% 'trialindices' - [cell array] indices of trials for each dataset.
% Default is all trials.
% 'recompute' - ['on'|'off'] force recomputing data file even if it is
% already on disk.
% 'rmcomps' - [integer array] remove artifactual components (this entry
% is ignored when plotting components). This entry contains
% the indices of the components to be removed. Default is none.
% 'interp' - [struct] channel location structure containing electrode
% to interpolate ((this entry is ignored when plotting
% components). Default is no interpolation.
% 'fileout' - [string] name of the file to save on disk. The default
% is the same name (with a different extension) as the
% dataset given as input.
%
% Outputs:
% customres - custom result array
%
% Authors: Arnaud Delorme, SCCN, INC, UCSD, Dec, 2020
% Copyright (C) Arnaud Delorme, SCCN, INC, UCSD, 2020, arno@ucsd.edu
%
% This file is part of EEGLAB, see http://www.eeglab.org
% for the documentation and details.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.
function std_custom(EEG, func, varargin)
if nargin < 1
help std_custom;
return;
end
% decode inputs
% -------------
options = varargin;
[g, funcopt] = finputcheck(options, { 'components' 'integer' [] [];
'channels' 'cell' {} {};
'timerange' 'float' [] [];
'recompute' 'string' { 'on','off' } 'off';
'savetrials' 'string' { 'on','off' } 'off';
'savefile' 'string' { 'on', 'off' } 'on';
'fileext' 'string' { } 'custom';
'trialindices' { 'integer','cell' } [] [];
'rmcomps' 'cell' [] cell(1,length(EEG));
'trialinfo' 'struct' [] struct([]);
'fileout' 'string' [] '';
'interp' 'struct' { } struct([]) }, 'std_spec', 'ignore');
if ischar(g), error(g); end
if isempty(g.trialindices), g.trialindices = cell(length(EEG)); end
if ~iscell(g.trialindices), g.trialindices = { g.trialindices }; end
if isfield(EEG,'icaweights')
numc = size(EEG(1).icaweights,1);
else
error('EEG.icaweights not found');
end
if ~isempty(g.channels)
filename = [ g.fileout '.dat' g.fileext];
prefix = 'chan';
else
filename = [ g.fileout '.ica' g.fileext];
prefix = 'comp';
end
if isempty(g.components)
g.components = 1:numc;
end
% No SPEC information found
% -------------------------
if isempty(g.channels)
[X,boundaries] = eeg_getdatact(EEG, 'component', [1:size(EEG(1).icaweights,1)], 'trialindices', g.trialindices );
else [X,boundaries] = eeg_getdatact(EEG, 'trialindices', g.trialindices, 'rmcomps', g.rmcomps, 'interp', g.interp);
end
if ~isempty(boundaries) && boundaries(end) ~= size(X,2), boundaries = [boundaries size(X,2)]; end
% get specific time range for epoched and continuous data
% -------------------------------------------------------
oritrials = EEG.trials;
if ~isempty(g.timerange)
if oritrials > 1
timebef = find(EEG(1).times >= g.timerange(1) & EEG(1).times < g.timerange(2) );
X = X(:,timebef,:);
EEG(1).pnts = length(timebef);
else
disp('warning: ''timerange'' option cannot be used with continuous data');
end
end
X = feval(func, X, funcopt{:});
% convert to structure
% --------------------
if strcmpi(prefix, 'comp')
savetofile( filename, X, 'comp', 1:size(X,1), options, {}, fileNames, g.trialindices, 'custom' , g.trialinfo);
else
if ~isempty(g.interp)
savetofile( filename, X, 'chan', 1:size(X,1), options, { g.interp.labels }, '', g.trialindices, 'custom' , g.trialinfo);
else
tmpchanlocs = EEG(1).chanlocs;
savetofile( filename, X, 'chan', 1:size(X,1), options, { tmpchanlocs.labels }, '', g.trialindices, 'custom' , g.trialinfo);
end
end
return;
% -------------------------------------
% saving SPEC information to Matlab file
% -------------------------------------
function savetofile( filename, X, prefix, comps, params, labels, dataFiles, dataTrials, datatype , trialInfo);
allcustom = [];
for k = 1:length(comps)
allcustom = setfield( allcustom, [ prefix int2str(comps(k)) ], squeeze(X(k,:,:)));
end
if nargin > 6 && ~isempty(labels)
allcustom.labels = labels;
end
allcustom.parameters = params;
allcustom.datatype = datatype;
allcustom.datafiles = dataFiles;
allcustom.datatrials = dataTrials;
allcustom.trialinfo = trialInfo;
allcustom.average_spec = mean(X,1);
std_savedat(filename, allcustom);
Datasets
Models
